Reducing data stored when using multiple information dispersal algorithms

ABSTRACT

Systems and methods for storing data in a dispersed storage network using at least two information dispersal algorithms (IDA&#39; s) having different widths and thresholds are disclosed. In multiple IDA configurations, at least two IDA&#39;s with different widths and thresholds are paired and used to store the data multiple times, where some IDA&#39;s provide “wider” IDA configurations that are more reliable and other IDA&#39;s provide “narrower” configurations with a lower threshold and lower reliability. Data can be written in the less reliable IDA configurations as a performance optimization to reduce the input/output operations necessary for reading the data. As a further optimization, the processing unit can determine to write only a subset of the IDA configurations. Similarly, dispersed storage units themselves, when reaching the capacity limits for their memory devices, can begin to delete slices they hold for some of the IDA configurations, to free up space.

CROSS REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/272,848 filed30 Dec. 2015, entitled “OPTIMIZING UTILIZATION OF STORAGE MEMORY IN ADISPERSED STORAGE NETWORK,” which is hereby incorporated herein byreference in its entirety and made part of the present U.S. UtilityPatent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks, and moreparticularly to dispersed or cloud storage.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on a remote orInternet storage system. The remote or Internet storage system mayinclude a RAID (redundant array of independent disks) system and/or adispersed storage system that uses an error correction scheme to encodedata for storage.

In a RAID system, a RAID controller adds parity data to the originaldata before storing it across an array of disks. The parity data iscalculated from the original data such that the failure of a single disktypically will not result in the loss of the original data. While RAIDsystems can address certain memory device failures, these systems maysuffer from effectiveness, efficiency and security issues. For instance,as more disks are added to the array, the probability of a disk failurerises, which may increase maintenance costs. When a disk fails, forexample, it needs to be manually replaced before another disk(s) failsand the data stored in the RAID system is lost. To reduce the risk ofdata loss, data on a RAID device is often copied to one or more otherRAID devices. While this may reduce the possibility of data loss, italso raises security issues since multiple copies of data may beavailable, thereby increasing the chances of unauthorized access. Inaddition, co-location of some RAID devices may result in a risk of acomplete data loss in the event of a natural disaster, fire, powersurge/outage, etc.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) in accordance with the presentdisclosure;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present disclosure;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present disclosure;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present disclosure;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present disclosure;

FIG. 6 is a schematic block diagram of an example of slice naminginformation for an encoded data slice (EDS) in accordance with thepresent disclosure;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present disclosure;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present disclosure;

FIG. 9 is a schematic block diagram of an example of a dispersed storagenetwork in accordance with the present disclosure;

FIG. 10A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentdisclosure; and

FIG. 10B is a flowchart illustrating an example of storing in adispersed storage network (DSN) in accordance with the presentdisclosure.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofdispersed storage (DS) computing devices or processing units 12-16, a DSmanaging unit 18, a DS integrity processing unit 20, and a DSN memory22. The components of the DSN 10 are coupled to a network 24, which mayinclude one or more wireless and/or wire lined communication systems;one or more non-public intranet systems and/or public internet systems;and/or one or more local area networks (LAN) and/or wide area networks(WAN).

The DSN memory 22 includes a plurality of dispersed storage units 36 (DSunits) that may be located at geographically different sites (e.g., onein Chicago, one in Milwaukee, etc.), at a common site, or a combinationthereof. For example, if the DSN memory 22 includes eight dispersedstorage units 36, each storage unit is located at a different site. Asanother example, if the DSN memory 22 includes eight storage units 36,all eight storage units are located at the same site. As yet anotherexample, if the DSN memory 22 includes eight storage units 36, a firstpair of storage units are at a first common site, a second pair ofstorage units are at a second common site, a third pair of storage unitsare at a third common site, and a fourth pair of storage units are at afourth common site. Note that a DSN memory 22 may include more or lessthan eight storage units 36.

DS computing devices 12-16, the managing unit 18, and the integrityprocessing unit 20 include a computing core 26, and network orcommunications interfaces 30-33 which can be part of or external tocomputing core 26. DS computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the dispersed storage units 36.

Each interface 30, 32, and 33 includes software and/or hardware tosupport one or more communication links via the network 24 indirectlyand/or directly. For example, interface 30 supports a communication link(e.g., wired, wireless, direct, via a LAN, via the network 24, etc.)between computing devices 14 and 16. As another example, interface 32supports communication links (e.g., a wired connection, a wirelessconnection, a LAN connection, and/or any other type of connectionto/from the network 24) between computing devices 12 and 16 and the DSNmemory 22. As yet another example, interface 33 supports a communicationlink for each of the managing unit 18 and the integrity processing unit20 to the network 24.

In general, and with respect to DS error encoded data storage andretrieval, the DSN 10 supports three primary operations: storagemanagement, data storage and retrieval. More specifically computingdevices 12 and 16 include a dispersed storage (DS) client module 34,which enables the computing device to dispersed storage error encode anddecode data (e.g., data object 40) as subsequently described withreference to one or more of FIGS. 3-8. In this example embodiment,computing device 16 functions as a dispersed storage processing agentfor computing device 14. In this role, computing device 16 dispersedstorage error encodes and decodes data on behalf of computing device 14.With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing or hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a DS computing devices 12-14. Forinstance, if a second type of computing device 14 has data 40 to storein the DSN memory 22, it sends the data 40 to the DS computing device 16via its interface 30. The interface 30 functions to mimic a conventionaloperating system (OS) file system interface (e.g., network file system(NFS), flash file system (FFS), disk file system (DFS), file transferprotocol (FTP), web-based distributed authoring and versioning (WebDAV),etc.) and/or a block memory interface (e.g., small computer systeminterface (SCSI), internet small computer system interface (iSCSI),etc.).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-16 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The managing unit 18 creates and stores user profile information (e.g.,an access control list (ACL)) in local memory and/or within memory ofthe DSN memory 22. The user profile information includes authenticationinformation, permissions, and/or the security parameters. The securityparameters may include encryption/decryption scheme, one or moreencryption keys, key generation scheme, and/or data encoding/decodingscheme.

The managing unit 18 creates billing information for a particular user,a user group, a vault access, public vault access, etc. For instance,the managing unit 18 tracks the number of times a user accesses anon-public vault and/or public vaults, which can be used to generateper-access billing information. In another instance, the managing unit18 tracks the amount of data stored and/or retrieved by a user deviceand/or a user group, which can be used to generate per-data-amountbilling information. As will be described in more detail in conjunctionwith FIGS. 10A and 10B, usage can be determined by a managing unit 18 ona byte-hour basis.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network operations can furtherinclude monitoring read, write and/or delete communications attempts,which attempts could be in the form of requests. Network administrationincludes monitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

To support data storage integrity verification within the DSN 10, theintegrity processing unit 20 (and/or other devices in the DSN 10 such asmanaging unit 18) may assess and perform rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. Retrieved encoded slices are assessed and checked for errorsdue to data corruption, outdated versioning, etc. If a slice includes anerror, it is flagged as a ‘bad’ or ‘corrupt’ slice. Encoded data slicesthat are not received and/or not listed may be flagged as missingslices. Bad and/or missing slices may be subsequently rebuilt usingother retrieved encoded data slices that are deemed to be good slices inorder to produce rebuilt slices. A multi-stage decoding process may beemployed in certain circumstances to recover data even when the numberof valid encoded data slices of a set of encoded data slices is lessthan a relevant decode threshold number. The rebuilt slices may then bewritten to DSN memory 22. Note that the integrity processing unit 20 maybe a separate unit as shown, included in DSN memory 22, included in thecomputing device 16, managing unit 18, stored on a DS unit 36, and/ordistributed among multiple storage units 36.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment (i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber. In the illustrated example, the value X11=aD1+bD5+cD9,X12=aD2+bD6+cD10, . . . X53=mD3+nD7+oD11, and X54=mD4+nD8+oD12.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as at least part of a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

In order to recover a data segment from a decode threshold number ofencoded data slices, the computing device uses a decoding function asshown in FIG. 8. As shown, the decoding function is essentially aninverse of the encoding function of FIG. 4. The coded matrix includes adecode threshold number of rows (e.g., three in this example) and thedecoding matrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a diagram of an example of a dispersed storage network. Thedispersed storage network includes a DS (dispersed storage) clientmodule 34 (which may be in DS computing devices 12 and/or 16 of FIG. 1),a network 24, and a plurality of DS units 36-1 . . . 36-n (which may bestorage units 36 of FIG. 1 and which form at least a portion of DSmemory 22 of FIG. 1), a DSN managing unit 18, and a DS integrityverification module (not shown). The DS client module 34 includes anoutbound DS processing section 81 and an inbound DS processing section82. Each of the DS units 36-1 . . . 36-n includes a controller 86, aprocessing module 84 (e.g. computer processor) including acommunications interface for communicating over network 24 (not shown),memory 88, a DT (distributed task) execution module 90, and a DS clientmodule 34.

In an example of operation, the DS client module 34 receives data 92.The data 92 may be of any size and of any content, where, due to thesize (e.g., greater than a few Terabytes), the content (e.g., securedata, etc.), and/or concerns over security and loss of data, distributedstorage of the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DS client module 34, the outbound DS processing section 81receives the data 92. The outbound DS processing section 81 processesthe data 92 to produce slice groupings 96. As an example of suchprocessing, the outbound DS processing section 81 partitions the data 92into a plurality of data partitions. For each data partition, theoutbound DS processing section 81 dispersed storage (DS) error encodesthe data partition to produce encoded data slices and groups the encodeddata slices into a slice grouping 96.

The outbound DS processing section 81 then sends, via the network 24,the slice groupings 96 to the DS units 36-1 . . . 36-n of the DSN memory22 of FIG. 1. For example, the outbound DS processing section 81 sendsslice group 1 to DS storage unit 36-1. As another example, the outboundDS processing section 81 sends slice group #n to DS unit #n.

In one example of operation, the DS client module 34 requests retrievalof stored data within the memory of the DS units 36. In this example,the task 94 is retrieve data stored in the DSN memory 22. Accordingly,and according to one embodiment, the outbound DS processing section 81converts the task 94 into a plurality of partial tasks 98 and sends thepartial tasks 98 to the respective DS storage units 36-1 . . . 36-n

In response to the partial task 98 of retrieving stored data, a DSstorage unit 36 identifies the corresponding encoded data slices 99 andretrieves them. For example, DS unit #1 receives partial task #1 andretrieves, in response thereto, retrieved slices #1. The DS units 36send their respective retrieved slices 99 to the inbound DS processingsection 82 via the network 24.

The inbound DS processing section 82 converts the retrieved slices 99into data 92. For example, the inbound DS processing section 82de-groups the retrieved slices 99 to produce encoded slices per datapartition. The inbound DS processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DS processing section 82 de-partitions the data partitions torecapture the data 92.

In one example of operation, the DSN of FIG. 1 is used to store datausing at least two information dispersal algorithms (IDA's) havingdifferent widths and thresholds. Explanations of this process are setout below in conjunction with FIGS. 10A and 10B. While described in thecontext of functionality provided by DS processing units 16, thisfunctionality may be implemented utilizing any module and/or unit of adispersed storage network (DSN) including the DS Managing Unit 18, theIntegrity Processing Unit 20, and/or one or more DS units 36 shown inFIG. 1.

In multiple IDA configurations, at least two IDA's with different widthsand thresholds are paired and used to store the data multiple times,where some IDA's provide “wider” IDA configurations that are morereliable, and other IDA's provide “narrower” configurations with a lowerthreshold and lower reliability. In some cases, the “narrowest”configuration could have a width and threshold of 1, which is equivalentto a copy of the data. Since the data stored in the less reliable IDAconfigurations is stored as a performance optimization to reduce theinput/output operations necessary for reading the data, it is not in allcircumstances, necessary to write the data in the less reliable IDAconfigurations. Therefore, a DS processing unit 16, upon reaching itsown limits of performance, in terms of CPU, bandwidth, memory, oranother resource limit, may opt to write only a subset of the IDAconfigurations. Similarly, the DS units 36 themselves, when reaching thecapacity limits for their memory devices, may begin to delete slicesthey hold for some of the IDA configurations, to free up space.

As a further optimization, they may prefer dropping slices fromconfigurations for which the higher reliability configuration is missingfew slices, as otherwise the reliability may be more severely impacted.

FIG. 10A is a schematic block diagram of another embodiment of adispersed storage network that includes two sets of storage unitsorganized as two storage sets 500-1 and 500-2, the network 24 of FIG. 1,and the DS processing unit 16 of FIG. 1. Each storage set may include anumber of storage units in accordance with corresponding dispersalparameters, where the dispersal parameters include an informationdispersal algorithm (IDA) width number and a decode threshold number,where the DS processing unit 16 dispersed storage error encodes data toproduce at least one set of encoded data slices, where the set ofencoded data slices includes the IDA width number of encoded dataslices, and where a decode threshold number of encoded data slices ofeach set of encoded data slices is required to recover the data. Forexample, the storage set 500-1 includes 42 storage units 36-1-1, 36-1-2. . . 36-1-42 when sets of encoded data slices are stored in the storageset 500-1, where each set of encoded data slices is associated with anIDA width of 42 of first dispersal parameters. Continuing with theexample, storage set 500-2 includes 8 storage units 36-2-1, 36-2-2 . . .36-2-8 when other sets of encoded data slices are stored in the storageset B, where each other set of encoded data slices is associated with anIDA width of 8 of second dispersal parameters. Each storage unit may beimplemented utilizing the DS execution unit 36 of FIG. 1. The DSNfunctions to store data.

In an example of operation of the storing of the data, the DS processingunit 16 dispersed storage error encodes data 502 utilizing the firstdispersal parameters (e.g., IDA width 42, decode threshold 22) toproduce a first plurality of sets of encoded data slices (e.g., one ormore sets of encoded data slices 504-1-504-42). The encoding includesselecting the first dispersal parameters based on one or more of apredetermination, a reliability requirement, and a request. For example,the DS processing unit 16 selects the first dispersal parameters toinclude the IDA width of 42 and the decode threshold of 22 to achieve ahigher than average level of storage reliability.

Having produced the first plurality of sets of encoded data slices, theDS processing unit 16 facilitates storage of the first plurality of setsof encoded data slices in a corresponding set of storage units. Thefacilitating includes identifying the corresponding set of storage units(e.g., interpreting system registry information), generating the one ormore sets of write slice requests that includes the first plurality ofsets of encoded data slices (504-1-504-42), and sending, via the network24, the one or more sets of write slice requests to the identifiedcorresponding set of storage units (e.g., storage units 36-1-1 to36-1-42 of the storage set 500-1).

Having stored the data 502 encoded using the first dispersal parameters,the DS processing unit 16 determines whether to encode the datautilizing second dispersal parameters. If so data 502 is dispersedstorage error encoded utilizing the second dispersal parameters toproduce the second plurality of sets of encoded data slices. Thedetermining may be based on one or more of encoding processing capacityavailability, storage capacity availability, network capacityavailability, and expected retrieval frequency of the data 502. Forexample, the DS processing unit 16 indicates to store the secondplurality of sets of encoded data slices when sufficient processingcapacity exists. As another example, the DS processing unit 16 indicatesto store the copy when a higher than average retrieval frequency levelfor the data 502 is expected and current network capacity levelavailability is lower than average (e.g., thus impeding accessing atleast 22 slices to recover data from the storage set 500-1).

For the second plurality of sets of encoded data slices, the DSprocessing unit 16 determines the second dispersal parameters. Thedetermining may be based on one or more of a predetermination, a dataretrieval performance requirement, and a request. For example, the DSprocessing unit 16 determines the second dispersal parameters to includethe IDA width of 8 and the decode threshold number of 5 when low networkutilization is desired, i.e. for fast access (e.g., only send accessrequests via the network 24 for 5 slices per set of slices).

Having determined the second dispersal parameters, the DS processingunit 16 dispersed storage error encodes the data 502 utilizing thesecond dispersal parameters to produce a second plurality of sets ofencoded data slices (e.g., one or more sets of encoded data slices).Having produced the second plurality of sets of encoded data slices, theDS processing unit 16 facilitates storage of the second plurality ofsets of encoded data slices in another corresponding set of storageunits. The facilitating includes identifying the other corresponding setof storage units, generating one or more sets of further write slicerequests that includes the second plurality of sets of encoded dataslices (506-1 to 506-8, and sending, via the network 24, the one or moresets of further write slice requests to the identified othercorresponding set of storage units (e.g., storage units 36-2-1 to 36-2-8of the storage set 500-2).

Having stored the second plurality of sets of encoded data slices the DSprocessing unit 16 determines whether to delete the second plurality ofsets of encoded data slices. For example, the DS processing unit 16indicates to delete the second plurality of sets of encoded data sliceswhen an actual data retrieval frequency level is lower than a retrievalthreshold level. As another example, the DS processing unit 16 indicatesto delete the second plurality of sets of encoded data slices when astorage availability level is less than a storage availability thresholdlevel. As yet another example, the DS processing unit 16 indicatesdelete the second plurality of sets of encoded data slices when anetwork capacity level is greater than a network capacity availabilitythreshold level. When deleting the second plurality of sets of encodeddata slices of the data, the DS processing unit 16 issues, via thenetwork 24, delete requests 508-1 to 508-8 to the other correspondingset of storage units (e.g., to storage units 36-2-1 to 36-2-8 of thestorage set B). The network of FIG. 1 could include more than two setsof storage units and data could be stored to more than one other storageset. While shown as separate and distinct storage sets using separatedispersed storage units, these sets could be logical and could use oneor more of the same storage units.

FIG. 10B is a flowchart illustrating an example of storing data. Themethod includes a step 600 where a processing module (e.g., of adistributed storage (DS) processing unit) encodes data utilizing thefirst dispersal parameters to produce a first reality of sets of encodeddata slices. The encoding includes selecting the first dispersalparameters based on one or more of a predetermination, a reliabilityrequirement, and a request. The method continues at the step 602 wherethe processing module facilitates storage of the first plurality of setsof encoded data slices in a first set of storage units. This can be doneby facilitating storage of respective sets of encoded data slices inrespective dispersed storage units of the first set of storage units.For example, the processing module identifies the corresponding set ofstorage units, generates one or more sets of write slice requests thatincludes the first plurality of sets of encoded data slices, and sendsthe one or more sets of write slice requests to the identifiedcorresponding sets of storage units.

The method continues at the step 604 where the processing moduledetermines whether to encode the data utilizing second dispersalparameters to produce a second plurality of sets of encoded data slices.The determining may be based on one or more of encoding processingcapacity availability, storage capacity availability, network capacityavailability, and an expected retrieval frequency of the data. Forexample, the processing module indicates to encode the data utilizingsecond dispersal parameters when sufficient processing capacity isavailable. As another example, the processing module indicates to storethe second plurality of sets of encoded data slices when a highretrieval frequency level is expected and network capacity availabilityis low.

The method continues at the step 606 where the processing moduledetermines second dispersal parameters for storing the second pluralityof sets of encoded data slices for the data. The determining may bebased on one or more of a predetermination, a data retrieval performancerequirement, and a request. The method continues at the step 608 wherethe processing module encodes the data utilizing the second dispersalparameters to produce a second plurality of sets of encoded data slices.The method continues at the step 610 where the processing modulefacilitates storage of the second plurality of sets of encoded dataslices in a second set of storage units. For example, the processingmodule identifies the other corresponding set of storage units (e.g.,which may be a subset of the first set of storage units), generates oneor more sets of further write slice requests that includes the secondplurality of sets of encoded data slices, and sends the one or more setsof further write slice requests to the identified other correspondingset of storage units.

The method continues at the step 612 of the processing module determineswhether to delete the second plurality of sets of encoded data slicesfor the data. For example, the processing module indicates to deletewhen actual data retrieval frequency is lower than a retrieval thresholdlevel. As another example, the processing module indicates to deletewhen a storage availability level is less than a storage availabilitythreshold level. As yet another example, the processing module indicatesto delete when a network capacity availability level is greater than anetwork capacity availability threshold level.

When deleting second plurality of sets of encoded data slices for thedata, the method continues at the step 614 where the processing moduleissues delete requests to the second set of storage units to facilitatedeletion of the second plurality of sets of encoded data slices. Forexample, the processing module generates the delete requests to includeslice names of the second plurality of sets of encoded data slices andsends the delete requests to the second set of storage units.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signalA has a greater magnitude than signal B, a favorable comparison may beachieved when the magnitude of signal A is greater than that of signal Bor when the magnitude of signal B is less than that of signal A. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from Figureto Figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information. A computer readable memory/storage medium,as used herein, is not to be construed as being transitory signals perse, such as radio waves or other freely propagating electromagneticwaves, electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method of storing data in a dispersed storage network, the dispersed storage network including a first set of dispersed storage units and a second set of dispersed storage units, the method comprising: encoding data utilizing first dispersal parameters to produce a first plurality of sets of encoded data slices; facilitating storage of respective sets of encoded data slices of the first plurality of sets of encoded data slices in respective dispersed storage units of the first set of dispersed storage units; encoding the data utilizing second dispersal parameters to produce a second plurality of sets of encoded data slices; and facilitating storage of respective sets of encoded data slices of the second plurality of sets of encoded data slices in respective dispersed storage units of the second set of dispersed storage units.
 2. The method of claim 1, further comprising selecting the first dispersal parameters based on one or more of a predetermination, a reliability requirement and a request.
 3. The method of claim 1, wherein the step of facilitating storage of respective sets of encoded data slices of the first plurality of sets of encoded data slices further comprises identifying the first set of dispersed storage units, generating at least one set of first write slice requests where the at least one set of first write slice requests include the first plurality of sets of encoded data slices, and sending the at least one set of first write slice requests to the first set of dispersed storage units.
 4. The method of claim 1, wherein the step of facilitating storage of respective sets of encoded data slices of the second plurality of sets of encoded data slices further comprises identifying the second set of dispersed storage units, generating at least one set of second write slice requests where the at least one set of second write slice requests include the second plurality of sets of encoded data slices, and sending the at least one set of second write slice requests to the second set of dispersed storage units.
 5. The method of claim 1, further comprising determining whether to encode the data utilizing second dispersal parameters based on one or more of encoding processing capacity availability, storage capacity availability, network capacity availability and expected retrieval frequency of the data.
 6. The method of claim 5, further comprising determining whether to delete the second plurality of sets of encoded data slices.
 7. The method of claim 6, further comprising at least one of: indicating to delete the second plurality of sets of encoded data slices when data retrieval frequency is lower than a first threshold; indicating to delete the second plurality of sets of encoded data slices when a storage availability level is less than a second threshold; or indicating to delete the second plurality of sets of encoded data slices when a network capacity availability level is greater than a third threshold.
 8. The method of claim 6, further comprising issuing delete requests to the second set of dispersed storage units.
 9. The method of claim 1, further comprising determining the second dispersal parameters based on one or more of a predetermination, a data retrieval performance requirement and a request.
 10. A dispersed storage processing unit for use in a dispersed storage network, the dispersed storage network including a first set of dispersed storage units and a second set of dispersed storage units, the dispersed storage processing unit comprising: a communications interface; a memory; and a computer processor; where the memory includes instructions for causing the computer processor to: encode data utilizing first dispersal parameters to produce a first plurality of sets of encoded data slices; facilitate storage of respective sets of encoded data slices of the first plurality of sets of encoded data slices in respective dispersed storage units of the first set of dispersed storage units; encode the data utilizing second dispersal parameters to produce a second plurality of sets of encoded data slices; and facilitate storage of respective sets of encoded data slices of the second plurality of sets of encoded data slices in respective dispersed storage units of the second set of dispersed storage units.
 11. The dispersed storage processing unit of claim 10, wherein the memory further comprises instructions for causing the computer processor to select the first dispersal parameters based on one or more of a predetermination, a reliability requirement and a request.
 12. The dispersed storage processing unit of claim 10, wherein the memory further comprises instructions for causing the computer processor to identify the first set of dispersed storage units, generate at least one set of first write slice requests where the at least one set of first write slice requests include the first plurality of sets of encoded data slices, and send the at least one set of first write slice requests to the first set of dispersed storage units.
 13. The dispersed storage processing unit of claim 10, wherein the memory further comprises instructions for causing the computer processor to identify the second set of dispersed storage units, generate at least one set of second write slice requests where the at least one set of second write slice requests include the second plurality of sets of encoded data slices, and send the at least one set of second write slice requests to the second set of dispersed storage units.
 14. The dispersed storage processing unit of claim 10, wherein the memory further comprises instructions for causing the computer processor to determine whether to encode the data utilizing second dispersal parameters based on one or more of encoding processing capacity availability, storage capacity availability, network capacity availability and expected retrieval frequency of the data.
 15. The dispersed storage processing unit of claim 14, wherein the memory further comprises instructions for causing the computer processor to determine whether to delete the second plurality of sets of encoded data slices.
 16. The dispersed storage processing unit of claim 15, wherein the memory further comprises instructions for causing the computer processor to at least one of: indicate to delete the second plurality of sets of encoded data slices when data retrieval frequency is lower than a first threshold; indicate to delete the second plurality of sets of encoded data slices when a storage availability level is less than a second threshold; or indicate to delete the second plurality of sets of encoded data slices when a network capacity availability level is greater than a third threshold.
 17. The dispersed storage processing unit of claim 15, wherein the memory further comprises instructions for causing the computer processor to issue delete requests to the second set of dispersed storage units.
 18. The dispersed storage processing unit of claim 10, wherein the memory further comprises instructions for causing the computer processor to determine the second dispersal parameters based on one or more of a predetermination, a data retrieval performance requirement and a request.
 19. A dispersed storage network comprising: a first set of dispersed storage units; a second set of dispersed storage units; and a disperse storage processing unit including: a communications interface; a memory; and a computer processor; where the memory includes instructions for causing the computer processor to: encode data utilizing first dispersal parameters to produce a first plurality of sets of encoded data slices; facilitate storage of respective sets of encoded data slices of the first plurality of sets of encoded data slices in respective dispersed storage units of the first set of dispersed storage units; encode the data utilizing second dispersal parameters to produce a second plurality of sets of encoded data slices; and facilitate storage of respective sets of encoded data slices of the second plurality of sets of encoded data slices in respective dispersed storage units of the second set of dispersed storage units.
 20. The dispersed storage network of claim 19, wherein the memory further comprises instructions for causing the computer processor to select the first dispersal parameters based on one or more of a predetermination, a reliability requirement and a request. 